Simultaneous Edit-Imputation for Continuous Microdata Package

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Description

The package implements an integrated editing and imputation for continuous microdata under linear constraints. It relies on a Bayesian nonparametric hierarchical modeling approach in which the joint distribution of the data is estimated by a flexible joint probability model. The generated edit-imputed data are guaranteed to satisfy all imposed edit rules, whose types include ratio edits, balance edits and range restrictions.

Details

Package: EditImputeCont
Type: Package
License: GPL (>= 3)

Author(s)

Quanli Wang, Hang J. Kim, Jerome P. Reiter, Lawrence H. Cox and Alan F. Karr

Maintainer: Quanli Wang <quanli@stat.duke.edu> and Hang J. Kim <hangkim0@gmail.com>

References

Hang J. Kim, Lawrence H. Cox, Alan F. Karr, Jerome P. Reiter and Quanli Wang (2015). "Simultaneous Edit-Imputation for Continuous Microdata", Journal of the American Statistical Association, DOI: 10.1080/01621459.2015.1040881.

See Also

readData, createModel, multipleEI

Examples

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library(EditImputeCont)

## read the toy example data, which has two ratio edits and a balance edit
data(SimpleEx)
data1 = readData(Y.original=SimpleEx$D.obs, ratio=SimpleEx$Ratio.edit, 
	range=NULL, balance=SimpleEx$Balance.edit)

## create and initialize the model with 15 DP mixture components
model1 = createModel(data.obj=data1, K=15)

## Run an iteration of MCMC

# model1$Iterate()

# dim(model1$Y.edited)
## [1]   1000   4  # Edit-imputed datasets of n=1000 records with p=4 variables

## Please see the example in the demo folder for more detailed explanation